Kavli Affiliate: Wei Gao
| First 5 Authors: Shangkun Sun, Bowen Qu, Xiaoyu Liang, Songlin Fan, Wei Gao
| Summary:
Recent advances in text-driven image editing have been significant, yet the
task of accurately evaluating these edited images continues to pose a
considerable challenge. Different from the assessment of text-driven image
generation, text-driven image editing is characterized by simultaneously
conditioning on both text and a source image. The edited images often retain an
intrinsic connection to the original image, which dynamically change with the
semantics of the text. However, previous methods tend to solely focus on
text-image alignment or have not aligned with human perception. In this work,
we introduce the Text-driven Image Editing Benchmark suite (IE-Bench) to
enhance the assessment of text-driven edited images. IE-Bench includes a
database contains diverse source images, various editing prompts and the
corresponding results different editing methods, and total 3,010 Mean Opinion
Scores (MOS) provided by 25 human subjects. Furthermore, we introduce IE-QA, a
multi-modality source-aware quality assessment method for text-driven image
editing. To the best of our knowledge, IE-Bench offers the first IQA dataset
and model tailored for text-driven image editing. Extensive experiments
demonstrate IE-QA’s superior subjective-alignments on the text-driven image
editing task compared with previous metrics. We will make all related data and
code available to the public.
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